# Causal Induction from Visual Observations for Goal Directed Tasks

arXiv: https://arxiv.org/abs/1910.01751 · HTML: https://mlsanity.com/paper/1910.01751/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 30 of 31)

- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery](https://mlsanity.com/paper/2103.02582/) (2021)
- [Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA](https://mlsanity.com/paper/2107.10098/) (2021)
- [Causal Dynamics Learning for Task-Independent State Abstraction](https://mlsanity.com/paper/2206.13452/) (2022)
- [Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning](https://mlsanity.com/paper/2107.00848/) (2021)
- [A Survey on Causal Reinforcement Learning](https://mlsanity.com/paper/2302.05209/) (2023)
- [Human Trajectory Prediction via Counterfactual Analysis](https://mlsanity.com/paper/2107.14202/) (2021)
- [Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning](https://mlsanity.com/paper/2207.09081/) (2022)
- [Causal Reinforcement Learning: A Survey](https://mlsanity.com/paper/2307.01452/) (2023)
- [Explainable Reinforcement Learning via a Causal World Model](https://mlsanity.com/paper/2305.02749/) (2023)
- [Passive learning of active causal strategies in agents and language models](https://mlsanity.com/paper/2305.16183/) (2023)
- [A Meta-Reinforcement Learning Algorithm for Causal Discovery](https://mlsanity.com/paper/2207.08457/) (2022)
- [Beyond Object Recognition: A New Benchmark towards Object Concept Learning](https://mlsanity.com/paper/2212.02710/) (2022)
- [Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning](https://mlsanity.com/paper/2406.03234/) (2024)
- [DROID: Driver-centric Risk Object Identification](https://mlsanity.com/paper/2106.13201/) (2021)
- [On the Generalization and Adaption Performance of Causal Models](https://mlsanity.com/paper/2206.04620/) (2022)
- [Learning Causal Overhypotheses through Exploration in Children and Computational Models](https://mlsanity.com/paper/2202.10430/) (2022)
- [TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators](https://mlsanity.com/paper/2105.04556/) (2021)
- [Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning](https://mlsanity.com/paper/2307.03486/) (2023)
- [CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning](https://mlsanity.com/paper/2401.16974/) (2024)
- [iReason: Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretability](https://mlsanity.com/paper/2107.10300/) (2021)
- [BusyBot: Learning to Interact, Reason, and Plan in a BusyBoard Environment](https://mlsanity.com/paper/2207.08192/) (2022)
- [Learning Causal Dynamics Models in Object-Oriented Environments](https://mlsanity.com/paper/2405.12615/) (2024)
- [BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning](https://mlsanity.com/paper/2407.10967/) (2024)
- [Did I do that? Blame as a means to identify controlled effects in reinforcement learning](https://mlsanity.com/paper/2106.00266/) (2021)
- [CauSight: Learning to Supersense for Visual Causal Discovery](https://mlsanity.com/paper/2512.01827/) (2025)
- [A Unified View of Algorithms for Path Planning Using Probabilistic Inference on Factor Graphs](https://mlsanity.com/paper/2106.10442/) (2021)
- [Visual Superordinate Abstraction for Robust Concept Learning](https://mlsanity.com/paper/2205.14444/) (2022)
- [Causality in the human niche: lessons for machine learning](https://mlsanity.com/paper/2506.13803/) (2025)
- [Using Human-Guided Causal Knowledge for More Generalized Robot Task Planning](https://mlsanity.com/paper/2110.04664/) (2021)
- [DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction](https://mlsanity.com/paper/2208.01373/) (2022)

## Abstract (arXiv)

Causal reasoning has been an indispensable capability for humans and other intelligent animals to interact with the physical world. In this work, we propose to endow an artificial agent with the capability of causal reasoning for completing goal-directed tasks. We develop learning-based approaches to inducing causal knowledge in the form of directed acyclic graphs, which can be used to contextualize a learned goal-conditional policy to perform tasks in novel environments with latent causal structures. We leverage attention mechanisms in our causal induction model and goal-conditional policy, enabling us to incrementally generate the causal graph from the agent's visual observations and to selectively use the induced graph for determining actions. Our experiments show that our method effectively generalizes towards completing new tasks in novel environments with previously unseen causal structures.